Systems and Methods for Tracking Non-Stationary Spectral Structure and Dynamics in Physiological Data

ABSTRACT

Systems and methods for tracking dynamic structure in physiological data are provided. In some aspects, the method includes providing physiological data, including electroencephalogram (“EEG”) data, acquired from a subject and assembling a time-frequency representation of signals from the physiological data. The method also includes generating a dynamic model of at least one non-stationary spectral feature, such as at least one non-stationary spectral peak, using the time-frequency representation and a user indication, and applying a dynamic model of at least one non-stationary spectral feature in a parameter estimation algorithm to compute concurrent estimates of spectral parameters describing the at least one non-stationary spectral feature. The method also includes tracking the spectral parameters of the at least one spectral feature over time.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is based on, claims priority to, andincorporates herein by reference in its entirety U.S. ProvisionalApplication Ser. No. 61/840,093 filed Jun. 27, 2013, and entitled“METHODS AND SYSTEMS FOR TRACKING NON-STATIONARY SPECTRAL PEAK STRUCTUREIN EEG DATA.”

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under DP2 OD006454awarded by the National Institutes of Health, and under IIS-0643995awarded by the National Science Foundation. The government has certainrights in the invention.

BACKGROUND

The present disclosure generally relates to systems and methods formonitoring and/or controlling states of a subject, and more specificallyto systems and methods for monitoring and/or controlling states of asubject using a model-based characterization of the dynamictime-frequency structure associated with physiological data.

In the case of surface recordings of brain activity, it was demonstratedover 75 years ago that central nervous system changes, such as thoseoccurring during sleep or as a result of administration of ananesthetic, are observable via neural EEG recordings, which measureelectrical impulses in the brain through electrodes placed on the scalp.As a consequence, it was postulated that EEG information could be usedto track in real time the brain states of patients, for instance duringsleep, or under sedation and general anesthesia, the same way that anelectrocardiogram could be used to track the state of the heart and thecardiovascular system.

Tools used by clinicians for monitoring brain states of patients includephysiologically and EEG-based systems developed to help measure neuralnetwork activity resulting from certain biological processes, taskactivity, sleep, anesthetic administration, and other clinicalprocedures. For example, such monitoring systems are used to track thelevel of consciousness of a patient undergoing general anesthesia orsedation in the operating room and intensive care unit. Usingproprietary algorithms that combine spectral and entropy informationderived from EEG data, such systems provide feedback through partial oramalgamized representations of the acquired signals for use inidentifying the brain state of a patient. In some scenarios, directmanipulation of the central nervous system, often performed usingpharmacological approaches, is facilitated using such systems by way ofcontrolling the level unconsciousness, amnesia, analgesia, andimmobility of a patient. For example, during sleep, EEG, EOG, EMG, andrespiration data is monitored in clinical or home settings, and thenevaluated through visual analysis to diagnose sleep and respiratorydisorders.

In order to examine specific spectral signatures of underlying neuralactivity, it has been an emerging practice to compute time-frequencyrepresentations of the acquired EEG data, using techniques including butnot limited to spectrograms (FFT, Hanning window), multitaperspectrograms, wavelet transforms, Gabor transforms, and chirplettransforms. Different approaches previously proposed characterizedmeasured neural rhythms at several discrete time periods, using methodsthat describe time-varying spectral signatures qualitatively. Forinstance, one attempt for tracking time-frequency features includedmodeling spectral content using pure sinusoids that have non-stationarypeaks and amplitudes. Specifically, the sum of more than one sinusoidmodel was used to track multiple simultaneously-evolving oscillations.While this previous approach may be adequate for tracking pure sinusoidsproduced by artificial or mechanical systems, such pure sinusoids arealmost never present in physiological systems.

For example, measured EEG signals often exhibit broadband peaks in thetime-frequency domain, the full structure of which can provide importantinformation about the underlying neural activity. In particular, thespecific form of a spectral peak, describing EEG data acquired during,say, administration of propofol during general anesthesia, can provideinformation about a patient's depth-of-consciousness. Hence, currentmethods that employ sinusoidal models of time-frequency structure arelacking since such methods collapse broadband physiological oscillationsto a single frequency, thereby ignoring information content present inthe peak bandwidth and structure. It is therefore necessary to devise asystem in which the full spectral structure and the informationcontained there in are retained.

Considering the above, there continues to be a need for systems andmethods to quantitatively and accurately analyze physiological datadynamics for monitoring patients and based thereon, provide systems andmethods for controlling patient states, such as during sedation, generalanesthesia, sleep, medically induced coma, hypothermia, drug delivery,or other natural or pharmacologically-mediated dynamic neural scenarios.

SUMMARY

The present disclosure overcomes drawbacks of previous technologies byproviding systems and methods for tracking the dynamic time-frequencystructure present in physiological data acquired from a subject, such asspectral peaks, for purposes including, data analysis, and monitoringand/or controlling physiological states of the subject. Specifically,the present disclosure provides systems and methods that utilize astatistical sampling approach to estimate, either concurrently orsimultaneously, parameters included in models describing dynamiccomponents of physiological signals in the time-frequency domain.

In particular, an approach is described that includes decomposingtime-varying spectra into multiple concurrent spectral peaks usingparametric or semi-parametric models of the peak structure, andcontemporaneously or simultaneously tracking their time-varyingproperties. Time-varying parameters of these spectral decompositionfunctions include spectral features such as instantaneous peakfrequency, amplitude, and bandwidth. In addition to the form of thisspecific model, a framework is provided by which such methodology can bebroadly applied to any physiological system using a wide array ofparametric or semi-parametric models of time-frequency structure andtemporal dynamics.

In this manner, the present disclosure provides an approach whereby itis possible retain important information contained in the full spectralstructure, by decomposing the power spectrum into specific components ofthe overall spectra, such as individual spectral peaks, each with aspecific shape as function of frequency, which could be broad-band. Such“spectral decomposition functions” are functional representations of thespectral components, defined using structurally-interpretable parameterssuch as the peak frequency, amplitude, bandwidth, as well as overallspectral shape of the oscillations. In doing so, a more completerepresentation of the power spectrum could be achieved. Moreover, sincethe frequency content of physiological systems may change over time,such spectral decomposition functions are allowed to vary in time aswell. This is achieved using a dynamic framework that allows thespectral decomposition function parameters to vary over time, perhaps asa function of intrinsic or extrinsic covariates.

In accordance with one aspect of the present disclosure, a system fortracking dynamic structure in physiological data is provided. The systemincludes at least one input configured to receive electroencephalography(“EEG”) data acquired from a subject, and a processor configured toreceive the EEG data from the at least one input and assemble atime-frequency representation of signals from the EEG data. Theprocessor is also configured to generate a dynamic model of at least onenon-stationary spectral peak using the time-frequency representation anda user indication, and apply the dynamic model in a parameter estimationalgorithm to compute concurrent estimates of peak parameters describingthe at least one non-stationary spectral peak, the peak parametersincluding a peak frequency, a peak bandwidth and a peak amplitude. Theprocessor is further configured to track the peak parameters of the atleast one non-stationary spectral peak over time.

In accordance with another aspect of the present disclosure, a methodfor tracking dynamic structure in physiological data is provided. Themethod includes providing electroencephalogram (“EEG”) data acquiredfrom a subject, and assembling a time-frequency representation ofsignals from the EEG data. The method also includes generating a dynamicmodel of at least one non-stationary spectral peak using thetime-frequency representation and a user indication, and applying adynamic model of at least one non-stationary spectral peak in aparameter estimation algorithm to compute concurrent estimates of peakparameters describing the at least one non-stationary spectral peak, thepeak parameters including a peak frequency, a peak bandwidth and a peakamplitude. The method further includes tracking the peak parameters ofthe at least one spectral peak over time.

In accordance with another aspect of the present disclosure, a systemfor tracking dynamic structure in physiological data is provided. Thesystem includes at least one input configured to receive physiologicaldata acquired from a subject and a processor configured to receive thephysiological data from the at least one input, and assemble atime-frequency representation of signals from the physiological data.The processor is also configured to generate a dynamic model of at leastone non-stationary spectral feature using the time-frequencyrepresentation and a user indication, and apply the dynamic model in aparameter estimation algorithm to compute concurrent estimates ofspectral parameters describing the at least one non-stationary spectralfeature. The processor is further configured to track the spectralparameters of the at least one non-stationary spectral feature overtime.

The foregoing and other advantages of the invention will appear from thefollowing description. In the description, reference is made to theaccompanying drawings which form a part hereof, and in which there isshown by way of illustration a preferred embodiment of the invention.Such embodiment does not necessarily represent the full scope of theinvention, however, and reference is made therefore to the claims andherein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereafter be described with reference to theaccompanying drawings, wherein like reference numerals denote likeelements.

FIGS. 1A-B are schematic block diagrams of a physiological monitoringsystem.

FIG. 2 is a schematic block diagram of an example system for improvedspectral analysis, in accordance with the present disclosure.

FIG. 3 is a flowchart setting forth steps for a process of trackingdynamic structure of signal data, in accordance with the presentdisclosure.

FIG. 4 is an illustration of an example monitoring and control system inaccordance with the present disclosure.

FIG. 5A is a graphical example illustrating simulated EEG data processedin accordance with the present disclosure.

FIG. 5B is a graphical illustration of the non-stationary peakparameters of FIG. 5A tracked in accordance with the present disclosure.

FIG. 6A is another graphical example illustrating measured EEG dataprocessed in accordance with the present disclosure.

FIG. 6B is graphical illustration of the non-stationary peak parametersof FIG. 6A tracked in accordance with the present disclosure.

DETAILED DESCRIPTION

Spectral analysis is an important tool for analyzing the time-frequencystructure of physiological data. The traditional approach to clinicalinterpretation of electrophysiological recordings is to visually examinetime domain waveforms, associating different waveform morphologies withphysiology, pathophysiology, or clinical outcomes. Visual time-seriesanalysis, however, is a highly subjective and time-consuming process,and spectral analysis can provide valuable information difficult toobserve in the time-domain. For example, in sleep medicine, sleepstudies record patient EEG, EMG, EOG, and respiratory data. These datarecords, lasting up to 10 hours in duration, are broken into 30-secondsegments, each of which must be visually interpreted in the time-domain.This scenario makes it extremely difficult to effectively tracknon-stationary properties of the sleep signal over time, which mayprovide important information for characterizing clinically-relevantfeatures of a patient's sleep. In another example, general anestheticand sedative drugs induce stereotyped non-stationary oscillations in theEEG that are much easier to interpret when analyzed in thetime-frequency domain using spectral analysis. The ability to track thetime-varying properties of these oscillations could be used to track,for example, changes in drug dosage or administration, or changes inpatient's level of arousal due to external stimuli.

Systems and methods, as provided herein, may be applied to physiologicaldata acquired from a subject under a number of clinical or experimentalscenarios, including but not restricted to sleep, drug delivery, generalor local anesthesia, sedation, coma, hyperthermia, and so on, forpurposes of monitoring and/or controlling the physiological state of thesubject. As will be described, the present disclosure details anapproach that quantitatively describes the dynamic time-frequencystructure of physiological signals, providing models that capturenon-stationary signal properties present therein, as well as respectivetemporal variation of such properties. Specifically, the time-frequencyrepresentation of acquired physiological data may be decomposed intoparametric, or semi-parametric functions specific to the forms of thespectral peak structures, the functions including parameters such as,instantaneous peak frequency, amplitude, and bandwidth. A time-varyingmodel may then be defined to describe the time variation of theparameters. As such, using an estimation procedure in conjunction withtime-frequency physiological data and models, the parameters may be fitat each point in time. In this manner, multiple concurrentnon-stationary components of a physiological signal can be tracked inthe time-frequency domain.

The following describes a specific implementation for analyzing EEG dataunder various scenarios, such as general anesthesia, sedation, or sleep,using a statistical sampling parameter estimation algorithm, Gaussianand Gamma shaped models of spectral peak structure, and a linearGaussian model of peak temporal dynamics. However, it may be appreciatedby one skilled in the art that various types physiological data acquiredunder multiple scenarios may be utilized, and be considered to be withinthe scope of the present invention. For instance, non-limiting examplesof physiological data may include data obtained fromelectroencephalography (“EEG”), electromyography (“EMG”),electrocorticography (“ECoG”), local field potentials (“LFP”),electrocardiography (“ECG”), electrooculography (“EOG”), galvanic skinresponse (“GSR”), oxygen saturation (SAO₂), ocular microtremor (“OMT”),and so forth. In addition, as will be described, the specific modelpresented relates to a more general statistical framework, which may beapplied in describing time-frequency dynamics of various physiologicalmonitoring conditions or scenarios.

Spectral Peak Parameter State Models

The following describes a specific implementation of an approach fordynamic time-frequency peak tracking, which is applied to simulated EEGdata, as well as EEG data acquired from a subject during theadministration of propofol anesthesia. This serves as a concrete examplein a general dynamic time-frequency tracking approach.

Given time-frequency observations, such as those generated from EEGsignal data, over discrete times t∈{1, . . . , T}, and fixed-widthfrequency bins centered at frequencies f∈{1, . . . , F}, a matrix can beconstructed of the spectral domain observations, namely

$\begin{matrix}{Y = \begin{pmatrix}y_{1,i} & \ldots & y_{1,T} \\\vdots & \ddots & \vdots \\y_{F,i} & \ldots & y_{F,T}\end{pmatrix}} & (1)\end{matrix}$

such that y_(t,f) is the magnitude of the power spectrum at time twithin the frequency bin f.

In general, Y may be any time-frequency representation, such as aspectrogram (FFT, Hanning window), multitaper spectrogram, wavelettransform, Gabor transform, or chirplet transform of any physiologicaldata, including EEG, EMG, ECoG, LFP, ECG, EOG, GSR, SAO₂, and OMT. In ageneral formulation, the y_(t,f) elements may include complex valuesrepresenting the magnitude and phase of the spectral domainobservations, as well as transformations such as a logarithmic ordecibel transformation, for instance. The data could also be transformedprior to computing the time-frequency representation, for instance, bycombining information across a plurality of electrodes across space, asin an EEG montage. Y may also include time-frequency data fromcombinations of various physiological data sources. y_(t,f)

In accordance with methods described in the present disclosure, anestimate of Y , namely Ŷ, is constructed from a function or a set offunctions with time-varying parameters, which may represent multipleconcurrent non-stationary spectral peaks. To compute ŷ_(t,f), which areestimates of y_(t,f), the spectral observations may be modeled as alinear combination of N discrete non-stationary peaks in spectral power,such that

$\begin{matrix}{{{\hat{y}}_{t,f} = {{\sum\limits_{n = 1}^{N}{\Omega_{n}\left( {t,f,\theta_{t}^{n}} \right)}} + ɛ_{y_{t,f}}}},} & (2)\end{matrix}$

where Ω_(n)(t,f,θ_(t) ^(n)) is the magnitude of the n^(th) spectralpeak, and ε_(y) _(t,f) is the observation noise at time t withinfrequency bin f. Each spectral peak is characterized at time t by itsown set of parameters θ_(t) ^(n), which describes its time-varyingamplitude A_(t), peak frequency F_(t), and bandwidth B_(t). Of note isthat this particular model defines the estimate of the time-frequencyrepresentation of the data as a linear mixture model. In general, Ŷ canbe formulated as any linear or non-linear combination of spectraldecomposition functions.

The temporal evolutions of the parameters may be defined as randomwalks, such that for a given parameter x

x_(t)=X_(t−1)+ε_(x) _(t) ,   (3)

where ε_(x) _(t)

N(0,σ_(x) _(t) ²).

The state variance parameters may also be defined as random walks,

σ_(x) _(t) ²=σ_(x) _(t−1) ²+ε_(v),   (4)

where ε_(v)

N(0, σ_(v) ²) and v is a constant.

In addition, the observation noise may be modeled as a function offrequency f

ε_(y) _(t,f) =N(0,σ_(y) _(t) ²f^(−p))   (5)

which reflects the 1/f^(p) noise phenomenon, of power factor p, widelyobserved in physiological EEG data. The observation noise variance alsoevolves as a random walk with constant variance

σ_(y) _(t) ²=σ_(y) _(t−1) ²+ε_(v),   (6)

where ε_(v)

N(0,σ_(v) ²) as in Eqn. 4. Thus, the parameter vector for each spectralpeak is

θ_(t) ^(n)={A_(t),F_(t),B_(t),σ_(A) _(t) ²,σ_(f) _(t) ²,σ_(B) _(t) ²}.

While the above-described model represents parameter temporal dynamicsas linear and having Gaussian noise, and observations as having a1/f^(p) noise, other linear or non-linear models with Gaussian ornon-Gaussian state and observation noise may be used as well. Moreover,interactions between specific parameters, as well as relationshipsbetween parameters and other data sources or external correlates, suchas drug concentration, may also be modeled. Additionally, spectral phasestructure may be modeled, in cases where y_(t,f) elements are complex.

Observation Model Components

Given a framework for characterizing the time-frequency representationof physiological data as the combination of spectral features, thefunctional forms of these features may then be defined. In particular,with respect to spectral features commonly observed in EEG data, givenθ_(t) ^(n), the observation model

$\begin{matrix}{{\Omega_{n}\left( {t,f,\theta_{t}^{n}} \right)} = {\exp \left( {A_{t} - \frac{\left( {f - F_{t}} \right)^{2}}{2B_{t}}} \right)}} & (7)\end{matrix}$

can be used to characterized the spectral peak as a Gaussian shapedfunction with amplitude exp(A_(t)), peak frequency F_(t), and bandwidthB_(t).

The Gaussian shaped function, however, may not always be appropriatemodel of peak structure. Since frequency, by definition, is bounded atzero, the structure of the spectral peaks with low peak frequencies maybe highly asymmetrical. Specifically, this is evident in the EEGslow/delta wave rhythms observed in sleep and general anesthesia.Therefore, an alternative example of a model for the peak structure maybe defined as:

$\begin{matrix}{{{\Omega_{n}\left( {t,f,\theta_{t}^{n}} \right)} = {\frac{\exp \left( A_{t} \right)}{v}\left\lbrack {f^{({k - 1})}{\exp \left( {- \frac{f}{\phi}} \right)}} \right\rbrack}},} & (8)\end{matrix}$

where v=exp(1−k)[(k−1)

](^(k−1)), k=1/_(2B) _(t) (√{square root over (F_(t) ²(4B_(t)+F_(t)²))}+2B_(t)+F_(t) ²) and

=1/_(2F) _(t) (√{square root over (F_(t) ²(4B_(t)+F_(t) ²))}−F_(t) ²).In this manner, spectral features based on the shape of a Gammadistribution with amplitude exp(A_(t)), peak frequency F_(t), bandwidthB_(t), and shape and scale parameters, k, and j, respectively, may becharacterized.

In general, various parametric models of spectral structures can beused, including those based on other continuous distributions such aslognormal, Gompertz, Chi-squared, inverse-Chi-squared, exponential,inverse Gamma, inverse Gaussian, Beta, and so on. Additionally,semi-parametric functions may also be used, to include splines andBézier curves, for example.

Likelihood and Goodness-of-Fit

Given N simultaneous, or concurrent, spectral features defined by afunction or set of functions, a parameter vector and observation noisevector can be constructed for each time t as follows:

$\begin{matrix}{\Theta_{t} = \left\{ {\theta_{t}^{1},\ldots \;,\theta_{t}^{N},\left\{ \sigma_{y_{t,f}}^{2} \right\}^{\overset{{t \in 1},\; \ldots \;,T}{{f \in 1},\; \ldots \;,F}}} \right\}} & (9)\end{matrix}$

For a given

_(t), the probability at time t of the observed data given the model,Pr(Y_(t)|

_(t)), which is proportion to the instantaneous likelihood, can becomputed as follows:

$\begin{matrix}{{{\Pr \left( Y_{t} \middle| \Theta_{t} \right)} \propto {L\left( \Theta_{t} \right)}} = {{\exp \left( {{- {\sum\limits_{f = 1}^{F}\frac{\left( {{{re}\left\{ y_{t,f} \right\}} - {{re}\left\{ {\hat{y}}_{t,f} \right\}}} \right)^{2}}{2\left( \sigma_{y_{t,f}}^{2} \right)}}} - {\sum\limits_{f = 1}^{F}\frac{\left( {{{im}\left\{ y_{t,f} \right\}} - {{im}\left\{ {\hat{y}}_{t,f} \right\}}} \right)^{2}}{2\left( \sigma_{y_{t,f}}^{2} \right)}}} \right)}.}} & (10)\end{matrix}$

In the spectral peak example described, the parameter vector may be

_(t)={θ_(t) ^(N),σ_(y) _(t) ²,P}  (11)

Since the y_(t,f) elements represent only the magnitude of thetime-frequency representation of the data, the imaginary component ofthe likelihood disappears, and σ_(y) _(t,f) ²=σ_(y) _(t) ²f^(−P), sothat the likelihood becomes:

$\begin{matrix}{{{\Pr \left( Y_{t} \middle| \Theta_{t} \right)} \propto {L\left( \Theta_{t} \right)}} = {{\exp \left( {- {\sum\limits_{f = 1}^{F}\frac{\left( {y_{t,f} - {\hat{y}}_{t,f}} \right)^{2}}{2\left( {\sigma_{y_{t}}^{2}f^{- p}} \right)}}} \right)}.}} & (12)\end{matrix}$

Given any dynamic model of time-frequency decomposition functions, thegeneral likelihood expression of Eqn. 10 and the data, a totallikelihood of the model L_(total)(

) given the data can be computed by taking the product of the likelihoodover time, or the sum of the log-likelihood over time:

$\begin{matrix}{{L_{total}(\Theta)} = {{\prod\limits_{t = 1}^{T}\; {L\left( \Theta_{t} \right)}} = {\sum\limits_{t = 1}^{T}{{\log \left( {L\left( \Theta_{t} \right)} \right)}.}}}} & (13)\end{matrix}$

The total likelihood may then be used to assess a goodness-of-fit, andto perform a model comparison. Specifically, it is possible in thisapproach to use a relative goodness-of-fit indicator for the differentselected, or generated, models to compare hypotheses related to dynamicfeatures of the underling physiological time-frequency structure, suchas a number of peaks, peak temporal dynamics, relationships between thestructure of different peaks, relationships between peak structure andexternal correlates, differences in peak structure across time/space,differences in peak structures across groups/pathologies/experimentalconditions, and so forth.

Therefore, in some aspects, the modeling framework presented herein maybe further implemented as a powerful tool for data analysis, as it maybe utilized for quantitative assessment for different theories relatedto the dynamical properties of a physiological system.

Parameter Estimation

Given physiological data, the model framework, and a metric forassessing goodness-of-fit, it is possible to provide estimates for themodel parameters. In one particular embodiment, as will be described, astatistical sampling method called a bootstrap particle filter may beapplied for estimating parameters. However, alternative estimationprocedures could also be employed, such as other sequential importancesampling (“SIS”) methods, Kalman filters, variational Bayes estimators,and the Expectation-Maximization (“EM”) algorithm. Additionally,explicit model-specific estimates can also be computed. Specifically,the purpose of such estimation procedures is to produce an estimate ofthe distribution of each of the model parameters during each time periodof a time-frequency representation. Given such distribution, statisticsrelated to individual or multiple spectral peaks present in the data canbe computed, changes in spectral structure can be tracked, computefunctions of this distribution that are related to clinical or diseasestates, and compute the statistical uncertainty of all of thesequantities.

Referring to the example spectral peak model presented herein, asmentioned, the parameter vector of Eqn. 11 and likelihood of Eqn. 12 maybe used to construct a bootstrap particle filter, which is a Bayesiansequential importance sampling method that generates a set of Pparameter vectors, or particles, whose distributions approximates theposterior distribution, Pr(Y_(t)|

_(t)). In some aspects, the initial particle values may be drawn from apre-defined proposal density, for example, using information provided bya user. Particularly, in dealing with physiological data underexperimental conditions, information relating to understoodcharacteristics of spectral peaks may be used to inform the choice ofproposal densities, since the time-frequency structure of the data maybe well-known. This allows judicious selection of priors for each of theparameters in questions, given specific knowledge of the underlyingphysiology such as number of peaks, peak frequency, amplitude, andbandwidth. For example, in the case of EEG during propofol anesthesia,generally two peaks arise in the time-frequency domain during loss ofconsciousness, and hence, N=2. Since the peak frequency, amplitude andbandwidth parameters of each of these oscillations have been previouslydescribed, proposal densities reflecting this knowledge may be used foreach of the peaks.

Therefore, for each spectral peak parameter x at time 0, a proposaldensity may be drawn in accordance with

Pr(x₀): U(x_(min),x_(max)),   (14)

where each parameter may be distributed uniformly between experimentallyknown bounds. For example, when human subjects close their eyes, an EEGoscillation is generally observed in the occipital portion of the brain,and exhibits a peak frequency that falls between 8-12 Hz. Therefore, aprior for peak frequency of this particular “alpha” oscillation could beuniform between 8 and 12 Hz.

In some aspects, it is also possible to use non-uniform distributions ofphysiologically-known parameter values, such as a Gaussian centereddistributions, or specialized distributions such as the betadistribution for binomial data or inverse-chi-squared for priors onvariance. By contrast, in case that no physiological precedent isavailable, a broad uniform density may be utilized. The multidimensionalproposal density for the entire parameter vector is called p(Q₀).

In accordance with the present invention, an iterative bootstrapparticle filter procedure may be performed as follows, given a set ofparticles P, where p_(t) ^(i) is the i^(th) particle at time t , andcontains values for Q_(t), the vector of all model parameters at time t.Specifically, a bootstrap particle filter procedure for the example EEGmodel described may include steps as follows:

1) Initialize the particles using the proposal densities, such that att=0, p₀ ^(i)˜π(

₀).

2) For each time t∈{2, . . . , T}, for all particles {p_(t) ¹, . . . ,p_(t) ^(P)}:

-   -   a) Sample a new value for each particle based on the one-step        prediction density, p_(t|t−1) ^(i)˜Pr(        _(t)|p_(t−1) ^(i)) by applying Eqn. 3 and 4. The absolute value        may be taken to ensure that each parameter is positive definite.    -   b) Compute a weight w^(i) such that w^(i)=L(p_(t|t−1) ^(i)), and        normalized so that

${\sum\limits_{i - 1}^{P}w^{t}} = 1.$

-   -   c) Resample the collection of particles according to the set of        weights Pr(p_(t) ^(i)=p_(t|t−1) ^(j))=w^(j).    -   d) The parameter estimate        may be defined as the component-wise median of {p_(t) ¹, . . . ,        p_(t) ^(P)}. Confidence bounds with a significance α can be        computed using the component-wise α/2 and 1−α/2 percentiles of        {p_(t) ¹, . . . , p_(t) ^(P)}.

An estimated filtered time-frequency representation may then bereconstructed using the parameter estimate {circumflex over (Θ)} andEqns. 2, 7, and 8, which describe the spectral decomposition functionsand the way in which they are combined to estimate the time-frequencyrepresentation of the data.

Referring now specifically to the drawings, FIGS. 1A and 1B illustrateexample monitoring systems and sensors that can be used to providephysiological monitoring of a subject during physiological processessuch as sleep, and under pharmacological-induced states such as generalanesthesia or sedation.

For example, FIG. 1A shows an embodiment of a physiological monitoringsystem 10. In the physiological monitoring system 10, a medical patient12 is monitored using one or more inputs, such as a sensor assembly 13,each of which transmits a signal over a cable 15 or other communicationlink or medium to a physiological monitor 17. In some aspects, an input(not shown) may be configured to receive an indication from a user. Thephysiological monitor 17 includes a processor 19 and, optionally, adisplay 11. The sensor assembly 13 includes physiological sensingelements such as, for example, electrical EEG sensors, EMG sensors, GSRsensors, depth electrodes, or the like. The sensor assembly 13 cangenerate respective signals by measuring physiological parameters of thepatient 12. The signals are then processed by one or more processors 19.The one or more processors 19 then communicate the processed signal tothe display 11 if a display 11 is provided. In an embodiment, thedisplay 11 is incorporated in the physiological monitor 17. In anotherembodiment, the display 11 is separate from the physiological monitor17. The monitoring system 10 is a portable monitoring system in oneconfiguration. In another instance, the monitoring system 10 is a pod,without a display, and is adapted to provide physiological parameterdata to a display.

For clarity, a single block is used to illustrate the sensor assembly 13shown in FIG. 1A. It should be understood that the sensor assembly 13shown is intended to represent one or more sensors and adapted toreceive signals from the patient 12. For example, the sensor assembly 13can include EEG, EMG, ECoG, LFP, ECG, EOG, GSR, and SAO₂ sensors, aswell as respiration sensors and other sensors used for otherphysiological recordings. Various combinations of numbers and types ofsensors, as mentioned, are suitable for use with the physiologicalmonitoring system 10.

In some embodiments of the system shown in FIG. 1A, all of the hardwareused to receive and process signals from the sensors are housed withinthe same housing. In other embodiments, some of the hardware used toreceive and process signals is housed within a separate housing. Inaddition, the physiological monitor 17 of certain embodiments includeshardware, software, or both hardware and software, whether in onehousing or multiple housings, used to receive and process the signalstransmitted by the sensor assembly 13.

As shown in FIG. 1B, each sensor 13 in a sensor assembly can include acable 25. The cable 25 can include three conductors within an electricalshielding. One conductor 26 can provide power to a physiological monitor17, one conductor 28 can provide a ground signal to the physiologicalmonitor 17, and one conductor 28 can transmit signals from the sensor 13to the physiological monitor 17. For multiple sensors, one or moreadditional cables 25 can be provided.

In some embodiments, the ground signal is an earth ground, but in otherembodiments, the ground signal is a patient ground, sometimes referredto as a patient reference, a patient reference signal, a return, or apatient return. In some embodiments, the cable 25 carries two conductorswithin an electrical shielding layer, and the shielding layer acts asthe ground conductor. Electrical interfaces 23 in the cable 25 canenable the cable to electrically connect to electrical interfaces 21 ina connector 20 of the physiological monitor 17. In another embodiment,the sensor 13 and the physiological monitor 17 communicate wirelessly.

Referring to FIG. 2, an example system 200 for use in carrying out stepsin accordance with the present disclosure, is illustrated. The system200 may include an input 202, a pre-processor 204, a spectral peaktracking engine 206, a physiological state analyzer 208, and an output210. Some or all of the modules of the system 200 can be implemented bya physiological patient monitor as described above with respect to FIG.1.

The input 202 may be configured to accept an indication from a userrelated to a particular subject profile such as a patient's age, height,weight, gender, or the like, as well as a drug administrationinformation, such as timing, dose, rate, and the like. In some aspects,the indication may also include information related to the physiologicalconditions or scenarios of a subject being monitored by system 200. Forexample, such physiological conditions may include the subject beingunder pharmacological-induced states, such as general anesthesia orsedation, or while asleep, or while undergoing a medical procedure.Additionally, the indication may include information directed toselection of particular spectral decomposition functions and temporalmodels that could describe physiological data acquired from the subject,including initial priors for model parameters chosen based on specifiedor identified physiological conditions or scenarios.

The pre-processor 204 may be designed to carry out any number ofprocessing steps for operation of system 200. In particular, thepre-processor 204 may be configured to receive physiological dataobtained via input 202 and assemble the data into time-series.Additionally, the pre-processor 204 may be capable of performing stepsfor removing interfering and/or undesired signals associated with thedata via signal rejection or filtering techniques. In some aspects, thepre-processor 204 may also be configured to assemble raw or processedsignals from acquired physiological data into time-frequencyrepresentations. For instance, the pre-processor 204 may process andassemble acquired EEG data, using, for example, a multi-taper approach,to produce spectrograms, or other representations of spectral content inthe data as a function of time. In some aspects, the pre-processor 204may also be configured to receive an indication from a user and performpre-processing steps in accordance with the indication.

In addition to the pre-processor 204, the system 200 further includes atracking engine 206, in communication with the pre-processor 202,designed to receive pre-processed data from the pre-processor 202 andcarry out steps necessary for identifying and tracking non-stationaryspectral features associated with acquired physiological data, includingspectral peaks. In general, the tracking engine 206 may provide timeestimates for specific spectral structure present in assembledspectrograms. For instance, spectrograms commonly include multiplespectral peaks occurring substantially concurrent or simultaneously intime, and hence the tracking engine 206 may provide estimates ofspectral peak features including instantaneous and time-evolutions oftarget parameter values, such as peak frequency, bandwidth, andamplitude. In this manner, temporal profiles of various spectralcharacteristics may be determined, which may then be used, in additionto other determined indicators, by the physiological state analyzer 208to identify physiological states of a subject. For example, sleep state,or a state of consciousness, or sedation, of patient underadministration of a drug with anesthetic properties, as well asconfidence indications with respect to the determined state(s) may bedetermined by the physiological state analyzer 208. Information relatedto the determined state(s) may then be relayed to the output 210, alongwith any other desired information, in any shape or form. In someaspects, the output 210 may include a display configured to provideinformation or indicators with respect to time variation ofnon-stationary features associated with physiological data, includingpeak parameters, that may be formulated using graphical, spectrogram, orother representations, either intermittently or in real time.

Turning to FIG. 3, a process 300 for tracking non-stationary spectralstructure in physiological data, in accordance with the presentdisclosure, is illustrated. In some aspects, the process 300 may beginat process block 302 where an indication from a user, provided usingsystems, in accordance with the present disclosure, may be received. Asdescribed, the indication may be related to selection of specificspectral decomposition functions, to include parametric orsemi-parametric functions, such as Gaussian or Gamma functions, as wellas other functions, and temporal dynamic models based on physiologicalconditions or scenarios. In addition, at process block 302, theindication may also include selection of initial priors in relation toparameter values describing the spectral decomposition functions andtemporal dynamic models. Alternatively, user-selected physiologicalconditions or scenarios, as well as subject characteristics, may elicituse of particular pre-programmed functions, models, priors, and otheroperational parameters, as described. In other aspects, pre-setconfigurations may be utilized with minimal user input.

At process block 304, desired amounts of physiological data may beprovided. In certain aspects, a time-series of physiological data isprovided and/or acquired from a subject, using, for example, systems asdescribed. Specifically, physiological data may be acquired during avariety of clinical or experimental scenarios, such as during sleep,drug delivery, general or local anesthesia, sedation, coma, hypothermia,and so forth. Non-limiting examples of provided physiological data atprocess block 304 may include any combination of EEG, EMG, ECoG, LFP,ECG, EOG, GSR, SAO₂, OMT, or other physiological recordings, generatedeither independently or in a substantially concomitant fashion.

Then, at process block 304, the time-series data may be used to assemblethe physiological signals into a time-frequency representation. Forexample, EEG data may be assembled into a spectrogram representationusing a multi-taper, or other, approach, although other representationsmay also be possible. Subsequently, at process block 308, the dynamicmodel of the spectral decomposition functions may be applied using anestimation procedure that computes concurrent estimates of spectralparameters. For example, spectral parameters describing target spectralpeaks, the spectral decomposition functions may be generated using aGaussian and/or Gamma-shaped functions, which are defined using aparametric representation that characterizes a peak frequency, peakbandwidth and peak amplitude.

Since the modeling framework provided herein characterizes the number ofpeaks, the spectral structure of each peak, and the temporal dynamics ofeach peak, this estimation procedure serves identify the peaks, or otherspectral features, in the data, as well as track them over time, in asubstantially concomitant fashion. In this manner, at process block 310,spectral parameters describing one or more spectral features associatedwith physiological data, such as spectral peaks, may be tracked overtime. Although, as described, Gaussian random walk models may be used todescribe the temporal dynamics of the spectral parameters, other linearand non-linear models, along with other noise distributions may be usedas well. In some aspects, steps associated with process blocks 304-310may be repeated, as desired or upon fulfillment of a terminationcondition.

At process block 312 a report may be generated of any shape or form. Forexample, a graphical illustration may be provided via a displayindicating a time evolution of parameters associated with one or morespectral features. Such report may be generated and/or updated insubstantially real time, as new physiologically data becomes available,or may be generated after all physiological data provided has beenprocessed, in accordance with the present disclosure. In some aspects,tracked parameters may be utilized to generate reconstructed, filtered,or denoised data using a spectrogram representation, or otherrepresentation. In other aspects, information related to trackedparameters, as well as other physiological (e.g. heart rate, behavioralresponse rate, sleep stage, and so on) or pharmacological (drug infusionrate, drug effect site concentration, and so on) correlates may bedisplayed, and/or used to provide feedback with respect to specificphysiological states of a subject. In yet other aspects, suchinformation may be used to control the state the subject, for example,by way of a continuous or intermittent control signal directed to anautomated or semi-automated control system, such as a drug deliverysystem, or by a provided indication to a clinician.

Referring now to FIG. 4, a system 410 in accordance with one aspect thepresent invention is illustrated. The system 410 includes a patientmonitoring device 412, such as a physiological monitoring device,illustrated in FIG. 4 as an EEG electrode array. However, it iscontemplated that the patient monitoring device 412 may also includemechanisms for monitoring EMG, ECoG, LFP, ECG, EOG, GSR, SAO₂, OMT andother physiological or behavioral data.

The patient monitoring device 412 is connected via a cable 414 tocommunicate with a monitoring system 416. Also, the cable 414 andsimilar connections can be replaced by wireless connections betweencomponents. As illustrated, the monitoring system 416 may be furtherconnected to a dedicated analysis system 418. Also, the monitoringsystem 416 and analysis system 418 may be integrated.

The monitoring system 416 may be configured to receive raw signalsacquired by the EEG, or other physiological, electrode array, andassemble, and even display, the raw signals as waveforms. Accordingly,the analysis system 418 may receive the physiological, or other,waveforms from the monitoring system 416 and, as will be described,process the waveforms and generate a report, for example, as a printedreport or, preferably, a real-time display of information. However, itis also contemplated that the functions of monitoring system 416 andanalysis system 418 may be combined into a common system. In one aspect,the monitoring system 416 and analysis system 418 may be configured todetermine a current and future brain state under physiologically statessuch as sleep, or during pharmacologically controlled conditions such asadministration of anesthetic compounds, such as during generalanesthesia or sedation.

In another aspect, the monitoring system 416 and analysis system 418 maybe configured to characterize a patient's biological orneurophysiological state by performing a statistical test to determinewhich of a set of dynamic and spectral decomposition models best fitsthe data. For example, during sleep, predefined models relating tonormal and pathological sleep could be applied to the datasimultaneously. The relative likelihood of those models given the datacould serve as a means of sleep pathology diagnosis. In another example,if applied to the administration of general anesthesia or sedation, theautomatic identification of patient-specific dynamics could greatlyimprove accuracy in drug delivery or the prediction of a patient'sfuture neural state.

The system 410 may also include a drug delivery system 420. The drugdelivery system 420 may be coupled to the analysis system 418 andmonitoring system 416, such that the system 410 forms a closed-loopmonitoring and control system, which could be based on a control signalderived from or incorporating the time-varying model parameters. Such aclosed-loop monitoring and control system in accordance with the presentinvention is capable of a wide range of operation, but includes userinterfaces 422 to allow a user to provide input or an indication,configure the closed-loop monitoring and control system, receivefeedback from the closed-loop monitoring and control system, and, ifneeded, reconfigure and/or override the closed-loop monitoring andcontrol system.

In general, a monitoring and/or control system, in accordance with thepresent invention, is capable of creating a closed-loop control systemfor delivering any pharmacological and non-pharmacological agent that iscorrelated with the dynamic time-frequency structure of physiologicaldata. Physiological states that may be controlled include sleep, generalanesthesia, sedation, medically-induced coma, hypothermia, and so on. Inone aspect of the invention, general anesthesia, sedation, or coma arecontrolled using a drug delivery system based on a control signalderived from the time-varying spectral decomposition function parametersestimated from physiological data. Non-limiting examples of drugs havinganesthetic properties include Propofol, Etomidate, Barbiturates,Thiopental, Pentobarbital, Phenobarbital, Methohexital, Benzodiazepines,Midazolam, Diazepam, Lorazepam, Dexmedetomidine, Ketamine, Sevoflurane,Isoflurane, Desflurane, Remifenanil, Fentanyl, Sufentanil, Alfentanil,and so on.

In some configurations, the drug delivery system 420 is not only able tocontrol the administration of anesthetic compounds for the purpose ofplacing the patient in a state of reduced consciousness influenced bythe anesthetic compounds, such as general anesthesia or sedation, butcan also implement and reflect systems and methods for bringing apatient to and from a state of greater or lesser consciousness.

In one aspect of the present disclosure, pharmacologically-induced sleepmay be controlled using the drug delivery system 420 based on a controlsignal derived from the time-varying spectral decomposition functionparameters estimated from physiological data, as described. Non-limitingexamples of drugs having properties used to aid or regulate sleepinclude zolpidem, eszopiclone, ramelteon, zaleplon, doxepine,benzodiazepines, antihistamines, and so on.

In another aspect of the invention, drugs administered to treatpsychiatric disorders, neurocognitive disorders, or neurologicaldisorders may be controlled using the drug delivery system 420 based ona control signal derived from the time-varying spectral decompositionfunction parameters estimated from physiological data.

In another aspect of the invention, drugs administered to regulatephysiological variables, such as heart rate, blood pressure,respiration, or blood oxygenation, may be controlled using the drugdelivery system 420 based on a control signal derived from thetime-varying spectral decomposition function parameters.

In another aspect of the invention, drugs administered to control painor nociception are controlled using the drug delivery system 420 basedon a control signal derived from the time-varying spectral decompositionfunction parameters estimated from physiological data. Non-limitingexamples classes of drugs having properties used to control pain ornociception include opioids, sympatholytics such as clonidine, and NMDAreceptor antagonists such as ketamine. By way of example, simulated data502 with a chirp-like spectrum, a Gaussian bandwidth structure, and a1/f observation noise was generated, shown in FIG. 5A. The simulationwas parameterized so that:

F_(t)=exp(m_(F)t+b_(F))

B_(t)=m_(B)t+b_(B)  (15)

A_(t)=exp(m_(A)t+b_(A))

over 10 minutes time sampled at 2 Hz. The parameters m_(F), m_(B), m_(A)were set to −1.2528, 2.2 and 1.9661, respectively, and b_(F), b_(B),b_(A) were set 1n(35), −20 and 1n(0.7), respectively. Using theseparameter values along with Eqn. 5, noise was added to each frequencyusing Eqn. 5 and σ_(y) ²=1.

As proof of concept, a bootstrap particle filter approach, in accordancewith the present disclosure, was applied to simulated data 502, whereby10,000 particles and broad uniform priors for all parameters were used.The filter output produced time varying estimates of the peak frequency506, peak amplitude 508, and peak bandwidth 510, shown graphically inFIG. 5B. The filter estimates the peak parameters well, with the truevalue falling (illustrated with dashed lines) falling within the 90%confidence bounds 99.92%, 99.58%, and 97.58% of the time for the peakfrequency, amplitude, and bandwidth, respectively. Consequently, thereconstructed spectrogram 504 shown in FIG. 5A strongly resembles thechirp in the original spectrogram. For the bandwidth, the estimate isinitially uncertain, as the signal to noise ratio is low for the largebandwidth/low amplitude portion of the chirp. As the chirp amplitudeincreases and the bandwidth narrows, the bandwidth confidence boundsbecome increasingly tighter.

By way of another example, a high-density (64-channel) EEG data set wascollected during administration of general anesthesia under the drugpropofol. In this experiment, the subject was brought out and in ofconsciousness using a computer-controlled infusion pump, which slowlyraised the concentration of propofol from a baseline of 0 mcg/ml to apeak level of 5 mcg/ml, then gradually returned the concentration backagain to 0 mcg/ml. In the present example, EEG data acquired from onesubject during a roughly two hour experiment was utilized, examining asingle Laplacian-referenced frontal channel.

A bootstrap particle filter approach, in accordance with the presentdisclosure, was applied to the experimental EEG data 602, shown in FIG.6A. Particularly, propofol data is especially suited for analysis, asdescribed herein, as it has two major oscillatory modes that changeduring the administration of general anesthesia, namely, the travelingpeak and the slow oscillation. The traveling peak is a rhythm thatappears during light anesthesia as a broadband, low amplitude spectralpeak at 15-25 Hz, then transitions to a more narrowband, high amplitudespectral peak at 8-12 Hz, as the concentration of propofol increases.The slow oscillation is a rhythm centered at less than 1.5 Hz with ahighly skewed peak structure, and amplitude that greatly increasesduring administration of propofol. This information was used to createpriors for each of the peaks.

The instantiation of the particle filter used 10000 particles tosimultaneously estimate both peaks, with Gaussian and Gammadistributions used for the traveling peak and slow oscillation,respectively. Priors were chosen based on knowledge of the physiologicalsystem. Specifically, for the traveling peak, the peak frequency andbandwidth priors were uniform random between 5 and 35 Hz, and between 0and 30 Hz, respectively. For the slow oscillation, the peak frequencyand bandwidth priors were uniform random between 0 and 5 Hz, and between0 and 3 Hz, respectively. The amplitude priors were uniform from 0 tothe maximum of the long of the data power.

As in the simulation example described above, the particle filterestimates for all three peak parameters, shown in FIG. 6B, had thegreatest uncertainty at the beginning and end of the experiment, whenthe amplitudes were lowest and the bandwidth was large. Overall, theestimates of peak frequency tracked the trends of the data peaks, forboth rhythms. The Gamma structure of the slow oscillation model allowedfor a reasonable reconstruction of a highly skewed spectral peak in thereconstructed spectrogram 604.

As described, the present disclosure provides a powerful tool forquantitative analysis of physiological data. Studies of relationshipsbetween estimated peak parameters and physiological correlates couldprovide insights into characterizing the dynamic processes governingphysiological activity.

Features from one or more of the above-described configurations may beselected to create alternative configurations comprised of asub-combination of features that may not be explicitly described above.In addition, features from one or more of the above-describedconfigurations may be selected and combined to create alternativeconfigurations comprised of a combination of features which may not beexplicitly described above. Features suitable for such combinations andsub-combinations would be readily apparent to persons skilled in the artupon review of the present application as a whole. The subject matterdescribed herein and in the recited claims intends to cover and embraceall suitable changes in technology.

1. A system for tracking dynamic structure in physiological data, thesystem comprising: at least one input configured to receiveelectroencephalography (“EEG”) data acquired from a subject; a processorconfigured to: (i) receive the EEG data from the at least one input;(ii) assemble a time-frequency representation of signals from the EEGdata; (iii) generate a dynamic model of at least one non-stationaryspectral peak using the time-frequency representation and a userindication; (iii) apply the dynamic model in a parameter estimationalgorithm to compute concurrent estimates of peak parameters describingthe at least one non-stationary spectral peak, the peak parametersincluding a peak frequency, a peak bandwidth and a peak amplitude; and(iv) track the peak parameters of the at least one non-stationaryspectral peak over time.
 2. The system of claim 1, wherein thetime-frequency representation includes a spectrogram representationindicative of a time variation in a spectral power distributiondescribing the signals.
 3. The system of claim 1, wherein the dynamicmodel characterizes dynamics of the at least one non-stationary spectralpeak using at least one spectral decomposition function.
 4. The systemof claim 1, wherein the processor is further configured to compute aposterior probability distribution, at a time t, of the peak parametersof the at least one non-stationary spectral peak given the physiologicaldata, the posterior probability distribution being proportional to aninstantaneous likelihood.
 5. The system of claim 4, wherein theprocessor is further configured to construct a set of particles in theparameter estimation algorithm, using the instantaneous likelihood andpeak parameters.
 6. The system of claim 5, wherein the processor isfurther configured to initialize the set of particles from a proposaldensity determined using information in accordance with one or both ofthe user indication or a physiological precedent.
 7. The system of claim5, wherein the processor is further configured to sample a new value foreach of the set of particles at a time tin accordance with a predictiondensity.
 8. The system of claim 5, wherein the processor is furtherconfigured to resample the set of particles according to normalizedweights computed using the instantaneous likelihood.
 9. The system ofclaim 5, wherein an estimate of peak parameters at a time t is definedas a component-wise median of the set of particles.
 10. The system ofclaim 5, wherein the processor is further configured to determineconfidence values for the peak parameters by computing component-wisepercentile values of the set of particles using a predeterminedsignificance.
 11. The system of claim 1, wherein the processor isfurther configured to generate a report indicative of a physiologicalstate of the subject using the tracked peak parameters.
 12. A method fortracking dynamic structure in physiological data comprising: providingelectroencephalogram (“EEG”) data acquired from a subject; assembling atime-frequency representation of signals from the EEG data; generating adynamic model of at least one non-stationary spectral peak using thetime-frequency representation and a user indication; applying a dynamicmodel of at least one non-stationary spectral peak in a parameterestimation algorithm to compute concurrent estimates of peak parametersdescribing the at least one non-stationary spectral peak, the peakparameters including a peak frequency, a peak bandwidth and a peakamplitude; and tracking the peak parameters of the at least one spectralpeak over time.
 13. The method of claim 12, wherein the time-frequencyrepresentation includes a spectrogram representation indicative of atime variation in a spectral power distribution describing the signals.14. The method of claim 12, wherein the dynamic model characterizesdynamics of the at least one non-stationary spectral peak using at leastone spectral decomposition function.
 15. The method of claim 13, furthercomprising computing a posterior probability distribution, at a time t,of the peak parameters of the at least one non-stationary spectral peakgiven the physiological data, the posterior probability distributionbeing proportional to an instantaneous likelihood.
 16. The method ofclaim 15, further comprising constructing a set of particles in theparameter estimation algorithm, using the instantaneous likelihood andpeak parameters.
 17. The method of claim 16 further comprisinginitializing the set of particles from a proposal density determinedusing information in accordance with one or both of the user indicationor a physiological precedent.
 18. The method of claim 16 furthercomprising sampling a new value for each of the set of particles at atime tin accordance with a prediction density.
 19. The method of claim16 further comprising resampling the set of particles according tonormalized weights computed using the instantaneous likelihood.
 20. Themethod of claim 16, wherein an estimate of peak parameters at a time tis defined as a component-wise median of the set of particles.
 21. Themethod of claim 16 further comprising determining confidence values forthe peak parameters by computing component-wise percentile values of theset of particles using a predetermined significance.
 22. The method ofclaim 12, further comprising acquiring the time-series of EEG data usingat least one sensor coupled to the subject experiencing anadministration of at least one drug having anesthetic properties, apsychiatric condition, a neurological condition, sleep, or anycombination thereof.
 23. The method of claim 12, further comprisinggenerating a report indicative of a physiological state of the subjectusing the tracked peak parameters.
 24. A system for tracking dynamicstructure in physiological data, the system comprising: at least oneinput configured to receive physiological data acquired from a subject;a processor configured to: (i) receive the physiological data from theat least one input; (ii) assemble a time-frequency representation ofsignals from the physiological data; (iii) generate a dynamic model ofat least one non-stationary spectral feature using the time-frequencyrepresentation and a user indication; (iii) apply the dynamic model in aparameter estimation algorithm to compute concurrent estimates ofspectral parameters describing the at least one non-stationary spectralfeature; and (iv) track the spectral parameters of the at least onenon-stationary spectral feature over time.
 25. The system of claim 24,wherein the physiological data includes electroencephalography (“EEG”)data.
 26. The system of claim 24, wherein the time-frequencyrepresentation includes a spectrogram representation indicative of atime variation in a spectral power distribution describing the signals.27. The system of claim 24, wherein the dynamic model characterizesdynamics of the at least one non-stationary spectral feature using atleast one spectral decomposition function.
 28. The system of claim 24,wherein the processor is further configured to compute a posteriorprobability distribution, at a time t, of the peak parameters of the atleast one non-stationary spectral peak given the physiological data, theposterior probability distribution being proportional to aninstantaneous likelihood.
 29. The system of claim 24, wherein theprocessor is further configured to apply a statistical samplingtechnique, a Kalman filtering technique, a variational Bayes estimatortechnique, and an Expectation-Maximization (“EM”) technique in theparameter estimation algorithm.
 30. The system of claim 24, wherein theprocessor is further configured to initialize parameter values from aproposal density determined using information in accordance with one orboth of the user indication or a physiological precedent.
 31. The systemof claim 24, wherein the spectral parameters include a peak frequency, apeak bandwidth and a peak amplitude
 32. The system of claim 24, whereinthe processor is further configured to determine confidence values forthe spectral parameters.
 33. The system of claim 24, wherein theprocessor is further configured to generate multiple dynamic modelsrelated to dynamic features of the physiological data, and compare thedynamic models using a relative goodness-of-fit indicator.
 34. Thesystem of claim 24, wherein the processor is further generate thedynamic model using information related to physiological correlatesincluding a drug level, a drug concentration, and a behavior, or anycombination thereof.
 35. The system of claim 24, wherein the processoris further configured to generate a report indicative of a physiologicalstate of the subject using the tracked spectral parameters.